Results Summary: random ILR Augmentation Methods - Pseudo-count \(0.5\)

Author

Oktawia Miluch

Published

October 14, 2025

1 Logistic regression with \(\text{L}_1\) penalty

1.1 Interactive Summary Table

The table below shows the mean of the selected performance metric for the chosen augmentation factor.

1.2 Boxplots of performance metrics

One tabset with a box plot corresponds to a single augmentation factor in the section below.

1.2.1 ROC AUC

1.2.1.1 Density default

1.2.1.2 Density = 0.1

1.2.1.3 Density = 0.5

1.2.2 Misclassification Rate

1.2.2.1 Density default

1.2.2.2 Density = 0.1

1.2.2.3 Density = 0.5

1.3 Impact of the augmentation factor on the predictive perfromance

The plots below show the impact of the augmentation factor on the selected performance metric. Each tabset corresponds to a given density of the skew-symmetric matrix used for augmentation.

1.3.1 ROC AUC

1.3.2 Misclassification Rate

2 Random Forest

2.1 Interactive Summary Table

The table below shows the mean of the selected performance metric for the chosen augmentation factor.

2.2 Boxplots of performance metrics

One tabset with a box plot corresponds to a single augmentation factor in the section below.

2.2.1 ROC AUC

2.2.1.1 Density default

2.2.1.2 Density = 0.1

2.2.1.3 Density = 0.5

2.2.2 Misclassification Rate

2.2.2.1 Density default

2.2.2.2 Density = 0.1

2.2.2.3 Density = 0.5

2.3 Impact of the augmentation factor on the predictive perfromance

The plots below show the impact of the augmentation factor on the selected performance metric. Each tabset corresponds to a given density of the skew-symmetric matrix used for augmentation.

2.3.1 ROC AUC

2.3.2 Misclassification Rate

3 XGBoost

3.1 Interactive Summary Table

The table below shows the mean of the selected performance metric for the chosen augmentation factor.

3.2 Boxplots of performance metrics

One tabset with a box plot corresponds to a single augmentation factor in the section below.

3.2.1 ROC AUC

3.2.1.1 Density default

3.2.1.2 Density = 0.1

3.2.1.3 Density = 0.5

3.2.2 Misclassification Rate

3.2.2.1 Density default

3.2.2.2 Density = 0.1

3.2.2.3 Density = 0.5

3.3 Impact of the augmentation factor on the predictive perfromance

The plots below show the impact of the augmentation factor on the selected performance metric. Each tabset corresponds to a given density of the skew-symmetric matrix used for augmentation.

3.3.1 ROC AUC

3.3.2 Misclassification Rate